Proteins that physically bind each other are generally involved in the execution of similar biological functions and shape similar phenotypes at the organismal level in health and disease. A protein interactome—the network of all possible protein interactions—constitutes an important intermediary step that could bridge the often difficult to cross chasm between genotype and phenotype, and is key in identifying drug targets.

A study conducted by scientists at Open Targets, European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI), and GlaxoSmithKline has uncovered genes linked to over 1000 human traits from 21 therapeutic areas, demonstrating that the process pinpoints drug targets or genes linked to diseases. The study reveals the shared basis of diseases using a map of interacting human proteins. By clarifying links between genetic mechanisms and human traits and diseases, the method can potentially prioritize candidate targets for drug discovery and identify opportunities for repurposing existing drugs.

The findings were published in the journal Nature Genetics on February 23, in an article titled “Network expansion of genetic associations defines a pleiotropy map of human cell biology.”

“This is an exciting showcase of one of our Open Targets collaborative informatics projects that has generated an array of new insights for novel target discovery as well as drug repurposing, and informs our understanding of the connection between rare and common diseases through shared biological processes,” said Ellen McDonagh, PhD, Director of Informatics Science at Open Targets.

Genome Wide Association Studies (GWAS) link genetic blueprints and disease phenotypes. To determine how genes contribute to disease, understanding protein function is critical since these are the workhorses in any cell.

Combining evidence from different sources, including EMBL-EBI’s IntAct database, Reactome, and Signor, the current study mapped the human protein interactome. This enabled the researchers to identify groups of interacting proteins that have been genetically linked through GWAS studies.


When little is known about the function of a novel protein, identifying its binding partners can reveal the biological processes it is involved in, offering clues for novel or alternative therapeutic targets. In this study, the researchers found 73 protein clusters linked to more than one trait or disease, a phenomenon known as pleiotropy. Pleiotropic relationships are invaluable to drug discovery because they indicate when a therapy for one disease might be effective in treating another. Pleiotropy can also point out targets to avoid that might trigger side effects.

“The interactome identified some known associations, such as cardiovascular diseases and lipoprotein or cholesterol measurements,” said Inigo Barrio Hernandez, PhD, a postdoctoral fellow at Open Targets and EMBL-EBI. “But we also found some unexpected associations. For example, the interactome highlighted three protein clusters shared by ten respiratory and skin immune-related diseases. This is hugely exciting because we now have some biological support to repurpose existing drugs that are proven to be safe to treat related diseases.”

Getting to the root

Protein network expansion is also a useful tool in gauging the relative importance of genes at loci identified through GWAS that compare common variations in the human genome between individuals with a specific trait or disease and control subjects. To get to the root cause of a disease, methods such as Open Target’s “locus-to-gene machine learning score” predict potential causative genes and proteins linked to traits and diseases. This score factors in the distance from the point of common variation to the gene-of-interest and the structure of the DNA in that region to prioritize relevant causative genes.

Using inflammatory bowel disease (IBD) as an example, the researchers showed that the interactome could be used to find proteins most likely to be involved in causing disease.

“This work bridges many fields of biology, including statistical genetics, cell biology, and bioinformatics,” said Pedro Beltrao, PhD, associate professor at ETH Zurich and former group leader at EMBL-EBI. “It brought together groups from across Open Targets and EMBL-EBI, and highlights the value of collaborations across disciplines.”

“This is now being developed further to provide tissue and cell-type specific networks to help further prioritize targets for disease treatment,” said McDonagh.

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